Middle Backend Python Engineer

About Shelf

The enterprise is going agentic and we are creating a unique operating system for an automated future.

Most AI agents break the moment they hit real business complexity. That's why we built a platform that models a company's policies, workflows, and operational logic into an AI Data Model. Shelf enables AI Agents to reliably reason and deliver precise and reliable outcomes at scale.

We already have what most AI startups are still trying to earn: Enterprise customers such as Glovo, Nespresso, and HelloFresh, real production data, and tens of thousands of users. Customers trust us with operational knowledge: Our platform is highly reliable and secure with SOC2, HIPAA-ready security practices, and mature integrations in place.

Job Description

This role is for a backend engineer with a few years of experience who wants to own real systems, not just implement tickets.

You will take on meaningful backend problems, work out the right approach, ship it, and help keep it healthy in production. We want someone who is technically solid, communicates clearly, and is growing the judgment to make systems simpler, safer, and easier to evolve over time. You will own real work from design through production, with the support of a strong team around you.

The hard part is real: building reliable, production-grade systems for agentic AI is a genuinely unsolved problem, and you'd work on it with real customers already depending on the result. That mix - real customers betting on us and problems the industry hasn't figured out yet, at startup speed - is rare.

This is a role for someone in a high-growth chapter of their career, who wants to do the best work of their life, learn fast, and win as part of a team going all-in on a hard mission.

What You Will Own

• Build and ship backend services, APIs, data flows, and background processing for production systems

• Turn requirements into concrete technical plans, trade-offs, and execution

• Help own services after launch: reliability, observability, performance, and incident follow-through

• Make sound decisions around interfaces, data models, and how your services fit the larger system

• Write clear technical notes and diagrams when they help the team move faster

• Work closely with product, frontend, and platform engineers to deliver end-to-end outcomes

• Improve engineering leverage with AI tooling, automation, and internal workflows rather than using AI as a gimmick

• Contribute to a high quality bar through code review and design review

What Strong Performance Looks Like

• You move backend work forward with growing independence, asking for help at the right moments rather than waiting to be told what to do

• Your services get easier to operate and change as your design judgment grows

• You communicate trade-offs clearly and are a reliable teammate

• You use AI tools well: to accelerate analysis, implementation, debugging, writing, and repetitive work, while keeping a high verification bar

What We Are Looking For

• Around 3 to 5 years of backend engineering experience building production systems

• Strong Python skills and the ability to write clean, maintainable backend code

• A working grasp of distributed systems: concurrency, failure handling, data consistency, async work, and service boundaries, with the appetite to deepen it

• Hands-on experience with cloud infrastructure such as AWS, GCP, or Azure

• Comfort with SQL and NoSQL systems and a solid feel for schema design

• A security-conscious approach to engineering: you handle sensitive data carefully and think about the security implications of the systems and AI workflows you ship

• Ability to go from problem statement to a shipped, production-ready result with growing ownership

• Clear written and verbal communication. You can explain systems, trade-offs, and incidents without hiding behind jargon

• AI-native working style. You already use AI tools in your daily engineering workflow and want to keep pushing that further

Strong Plus

• Exposure to agentic systems: AI agents, tool-calling, orchestration, retrieval, or LLM-backed infrastructure

• Working knowledge of TypeScript or the ability to contribute across the stack when needed

• A track record of growing fast and taking on more than your title strictly required

How We Evaluate Fit

We care more about ownership, systems judgment, and learning velocity than a perfect keyword match to our stack. If you are the kind of engineer who can take a messy problem and turn it into a strong production system, we want to talk.

What Shelf Offers

• B2B contract

• Company stock options

• Hardware: MacBook Pro

• Moderntechnical stack. Develop open-source software

• A strong AI-native engineering environment with modern tools and room to experiment, including Claude Code, OpenAI Codex, and GitHub Copilot

Why Shelf

• Becoming one of the defining companies of the AI age is a hard plan, and our leadership team has the rare mix of deep AI, knowledge-management, and enterprise SaaS experience to actually execute it

• We have raised over $60 million in funding; our investors include Tiger Global, Insight Partners, Base10, and others

• Recognized by Gartner as a Cool Vendor, with high-velocity growth powered by the most innovative product in our category

• We love our customers and our customers love us. Ask a Shelf customer why, and they'll tell you it's our innovative capabilities and rock-solid reliability, that they enjoy working with our people, and most of all, the improvements they see in their business KPIs

• We're building fast across three hubs: our New York HQ and our Product and Engineering centers in Warsaw and Lviv